Soil moisture measurements are needed in a large number of applications such
as hydro-climate approaches, watershed water balance management and
irrigation scheduling. Nowadays, different kinds of methodologies exist for
measuring soil moisture. Direct methods based on gravimetric sampling or time
domain reflectometry (TDR) techniques measure soil moisture in a small volume
of soil at few particular locations. This typically gives a poor description
of the spatial distribution of soil moisture in relatively large agriculture
fields. Remote sensing of soil moisture provides widespread coverage and can
overcome this problem but suffers from other problems stemming from its low
spatial resolution. In this context, the DISaggregation based on Physical And
Theoretical scale CHange (DISPATCH) algorithm has been proposed in the
literature to downscale soil moisture satellite data from 40 to 1 km
resolution by combining the low-resolution Soil Moisture Ocean Salinity
(SMOS) satellite soil moisture data with the high-resolution Normalized
Difference Vegetation Index (NDVI) and land surface temperature (LST)
datasets obtained from a Moderate Resolution Imaging Spectroradiometer
(MODIS) sensor. In this work, DISPATCH estimations are compared with soil
moisture sensors and gravimetric measurements to validate the DISPATCH
algorithm in an agricultural field during two different hydrologic scenarios:
wet conditions driven by rainfall events and wet conditions driven by local
sprinkler irrigation. Results show that the DISPATCH algorithm provides
appropriate soil moisture estimates during general rainfall events but not
when sprinkler irrigation generates occasional heterogeneity. In order to
explain these differences, we have examined the spatial variability scales of
NDVI and LST data, which are the input variables involved in the downscaling
process. Sample variograms show that the spatial scales associated with the
NDVI and LST properties are too large to represent the variations of the
average soil moisture at the site, and this could be a reason why the DISPATCH
algorithm does not work properly in this field site.

Soil moisture measurements taken over different spatial and temporal scales
are increasingly required in a wide range of environmental applications,
which include crop yield forecasting (Holzman et al., 2014),
irrigation planning (Vellidis et al., 2016), early
warnings for floods and droughts (Koriche and Rientjes, 2016), and
weather forecasting (Dillon et al.,
2016). This is mostly due to the fact that soil moisture controls the water
and energy exchanges between key environmental compartments (atmosphere and
earth) and hydrological processes, such as precipitation, evaporation,
infiltration and runoff (Ochsner, 2013; Robock et al.,
2000).

There are several applications in which soil moisture measurements have been
shown to provide relevant information (Robock et al., 2000). For example,
in environmental applications, soil moisture is typically used for defining
the water stress occurring in natural and human systems
(Irmak et al., 2000) or for
quantifying nitrate leaching and drainage quality
(Clothier and Green, 1994). Here, we highlight
that soil moisture measurements from the root zone yields important
information for field irrigation scheduling, determining to a great extent
the duration and frequency of irrigation needed for plant growth as a
function of water availability (Blonquist
et al., 2006; Jones, 2004; Campbell, 1982).

Soil moisture is highly variable in space and time, mainly as a result of the
spatial variability in soil properties (Hawley, 1983), topography (Burt and
Butcher, 1985), land uses (Fu and Gulinck, 1994), vegetation (Le Roux et al.,
1995) and atmospheric conditions (Koster and Suarez, 2001). As a result, soil
moisture data exhibits a strong scale effect that can substantially affect
the reliability of predictions, depending on the method of measurement used.
For this reason, it is important to understand how to measure soil moisture
for irrigation scheduling.

Nowadays, available techniques for measuring or estimating soil moisture can
provide data either at a small or at a large scale. Gravimetric measurements
(Gardner, 1986) estimate soil moisture by the difference between the natural
and the dry weight of a given soil sample. They are used as a reference
value of soil moisture for sensor calibration (Starr and Paltineanu, 2002)
or soil moisture validation studies (Bosch
et al., 2006; Cosh et al., 2006). The main disadvantage of this method is
that these measurements are time-consuming; users have to go to the field to
collect soil samples and place them in the oven for a long time. Soil
moisture sensors such as time domain reflectometry sensors
(Clarke Topp and
Reynolds, 1998; Schaap et al., 2003; Topp et al., 1980) or capacitance
sensors (Bogena
et al., 2007; Dean et al., 1987) are capable of measuring soil moisture
continuously using a data logger, thereby enabling the final user to save
time. Soil moisture sensors are especially useful for studying processes at
a small scale, but suffer from the typical low number of in situ sensors
that provide an incomplete picture of a large area
(Western et al., 1998).
Nevertheless, the use of soil moisture sensors is a common practice for
guiding irrigation scheduling in cropping field systems (Fares
and Polyakov, 2006; Thompson et al., 2007; Vellidis et al., 2008).

Remote sensing can estimate soil moisture continuously over large areas
(Jackson et al., 1996). In this
case, soil moisture estimations refer to the near-surface soil moisture
(NSSM), which represents the first 5 cm (or less) of the top soil profile.
In recent years, remote sensing techniques have improved and diversified
their estimation, making them an interesting tool for monitoring NSSM and
other variables such as the Normalized Difference Vegetation Index (NDVI)
and the land surface temperature (LST). Different satellites exist that are
capable of estimating NSSM: the Soil Moisture Active Passive (SMAP)
satellite, the Advanced Scatterometer (ASCAT) remote sensing instrument on
board the Meteorological Operational (METOP) satellite, the Advanced
Microwave Scanning Radiometer 2 (AMSR2) instrument on board the Global
Change Observation Mission 1-Water (GCOM-W1) satellite, and the Soil
Moisture and Ocean Salinity (SMOS) satellite launched in November 2009
(Kerr et al., 2001). The SMOS satellite has
global coverage and a revisit period of 3 days at the Equator, giving two
soil moisture estimations, the first one taken during the ascending overpass
at 06:00 LST (local solar time) and the second one during the descending
overpass at 18:00 LST. The SMOS satellite is a passive 2-D interferometer
operating at L-band frequency (1.4 GHz) (Kerr et al., 2010). The spatial
resolution ranges from 35 to 55 km, depending on the incident angle. Its
goal is to retrieve NSSM with a target accuracy of a 0.04 m3 m−3
(Kerr et al., 2012). Since SMOS NSSM values
have been validated on a regular basis since the beginning of its mission
(Bitar et al., 2012; Delwart et al.,
2008), they is considered suitable for hydro-climate applications (Lievens
et al., 2015; Wanders et al., 2014).

The relatively large variability of soil moisture compared to the low
resolution of SMOS-NSSM data hinders the direct application of this method
to irrigation scheduling. However, the need for estimating NSSM with a
resolution higher than 35–55 km using remote sensing has increased for
different reasons: (1) data are freely available, (2) a field installation of
soil moisture sensors is not necessary, and (3) no specific maintenance is
needed. For these reasons, in the last few years, different algorithms have
been developed to downscale remote sensing soil moisture data to tens or
hundreds of meters.

Chauhan et al. (2003) developed a Polynomial fitting method which estimates soil moisture
at 1 km resolution (Carlson, 2007; Wang and Qu,
2009). This method links soil moisture data with surface temperature,
vegetation index and albedo. It does not require in situ measurements but
cannot be used under cloud coverage conditions. The improvements in the
detection method reported by Narayan et al. (2006)
downscales soil moisture at 100 m resolution. This is an optimal resolution
for agricultural applications, but the method is highly dependent on the
accuracy of its input data. The same problem is attributed to the Baseline
algorithm for the SMAP satellite
(Das and Mohanty, 2006),
which downscales soil moisture at 9 km resolution. These algorithms have to
be validated using in situ measurements. For this purpose, most studies use
soil moisture sensors installed at the top soil profile, i.e., the first
5 cm of soil (Albergel et al., 2011; Cosh et al., 2004; Jackson et al.,
2010), while others use gravimetric soil moisture measurements
(Merlin et al., 2012) or the
combination of both methodologies (Robock et al., 2000). Satellite soil
moisture has recently been used to provide irrigation detection signals
(Lawston et al., 2017), quantify the amount of water applied (Brocca et al.,
2018; Zaussinger et al., 2018) and estimate the water use (Zaussinger et al.,
2018). All these deal with relatively homogeneous and extensive irrigation
surface coverages (several kilometers).

Other satellites, such as Sentinel-1, can estimate NSSM at 1 km resolution
(Bauer-Marschallinger et al., 2018; Hornacek et al., 2012; Mattia et al.,
2015; Paloscia et al., 2013). Sentinel-1 provides two kinds of products, the
first one is the Single Look Complex (SLC) algorithm and the second one is
the Ground Range Detected (GRD) algorithm. The latter can be used for solving
a wide range of problems related to Earth surface monitoring, such as soil
moisture, but it is not a direct measurement and therefore data processing is
needed. In this case, the GRD product is converted into radar backscatter
coefficients and then into decibels to estimate soil
moisture. Usually, these conversions are cumbersome because these kind of
measurements have surface roughness and vegetation influence that affect the
signal (Garkusha et al., 2017; Wagner et al., 2010).

The DISPATCH method (DISaggagregation based on Physical And Theoretical
CHange) (Merlin et al., 2008, 2012) is an algorithm that downscales SMOS NSSM
data from 40 km (low resolution) to 1 km resolution (high resolution). This algorithm
uses Terra and Aqua satellite data to estimate NDVI and LST twice a day
using the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor.
These estimations have a resolution of 1 km and can be conducted only if
there is no cloud cover. This downscaling process provides the final user
with the possibility of estimating NSSM using remote sensing techniques at
high resolution. DISPATCH successfully reveals spatial heterogeneities such as
rivers, large irrigation areas and floods (Escorihuela
and Quintana-Seguí, 2016; Malbéteau et al., 2015, 2017; Molero et
al., 2016) and it has also been validated (Malbéteau
et al., 2015; Merlin et al., 2012; Molero et al., 2016) in fairly large and
homogeneous irrigation areas, but it has not been applied in complex
settings with spatially changing hydrologic conditions such as those
representing a local irrigation field.

In this work, we evaluate the value of remote sensing in agricultural
irrigation scheduling by comparing in situ soil moisture data obtained from
gravimetric and soil moisture sensors, with soil moisture data determined by
downscaling remote sensing information with the DISPATCH algorithm.

Study area

The study area shown in Fig. 1 is located in the village of Foradada
(1.015∘ N, 41.866∘ W), in the Segarra–Garrigues (SG) system
(Lleida, Catalonia). The SG system is an important irrigation development
project currently being carried out in the province of Lleida, Catalonia,
which involves converting most of the current dry-land fields into irrigated
fields. Its construction enables 1000 new hectares with a long agricultural
tradition to be irrigated. To achieve this, an 85 km long channel was
constructed to supply water for irrigation. At present, approximately 16 000
irrigators are potential beneficiaries of these installations. However, most
farmers have not yet installed this irrigation system, which means that the
SG systems can still be regarded as dry land.

Figure 1Location of the Foradada field site within the Segarra-Garriga
irrigation system and distribution of soil moisture measurement points.
Gravimetric measurement points are arranged with cross section points in
green and support points in yellow. The location of EC-5 sensors are
represented in red.

The Urgell area is located in the west of the SG system. This area has
totally different soil moisture conditions, especially during the summer
season when the majority of fields are currently irrigated. This gives rise
to two clearly distinguishable wet and dry soil moisture conditions. Figure 1
shows the Foradada field, which represents 25 ha of a commercial field
irrigated by a solid set sprinkler irrigation system distributed across 18
different irrigation sectors. The soil texture, in a single point, is
65.6 % clay, 17.6 % silt and 16.8 % sand. Every year two
different crops are grown, the first one during the winter and spring
seasons, when wet conditions are maintained by precipitation, and the second
one during the summer and autumn seasons, when wet conditions are maintained
by sprinkler irrigation. The Foradada field is thus one of the few irrigated
fields located within the SG system. Consequently, this field has soil
moisture conditions similar to those in the surrounding area during the
winter and spring season, but completely different conditions during the
summer and autumn seasons. This makes this site unique for assessing remote
sensing in a distinct isolated irrigation field.

2.1 In situ soil moisture measurements

A total of nine intensive and strategic field campaigns were conducted in the
study area during 2016: DOY42, DOY85, DOY102, DOY187, DOY194, DOY200, DOY215,
DOY221 and DOY224. During each field campaign, disturbed soil samples were
collected from the top soil profile (0–5 cm depth) for measuring
gravimetric soil moisture data. A total of 101 measurement points, depicted
in Fig. 1, were defined around the field. They are divided into two different
kinds of points: (1) cross section points – 75 points defined to represent
the spatial variability of soil moisture in different cross sections (in
these cross sections, points are separated by 9, 16 and 35 m); (2) support
points – 26 points defined to complement information measured from cross
sections, thereby adding and supporting information about the spatial
variability across the field. Each soil sample is analyzed using the
gravimetric method for measuring gravimetric soil moisture content, which is
transformed to volumetric soil moisture content using bulk density
measurements (Letelier, 1982). Daily averages of gravimetric measurements and
their standard deviations were computed to represent the soil moisture
associated with the entire field site.

Soil moisture was also measured using capacitive EC-5 sensors (METER Group,
Pullman, WA, USA), previously calibrated in the laboratory (Star and
Paltineanu, 2002). As Fig. 1 shows, a total of five control points were
installed across one of the three gravimetric cross sections. Each control
point represents a different irrigation sector of the field. Soil moisture
sensors were installed at 5 cm depth, taking into account the measured
volume of these sensors. Their accuracy is ±0.03 cm3 cm−3
(Campbell and Devices, 1986). They were connected to an EM50G data logger
(METER Group, Pullman, WA, USA) that registers soil moisture every 5 min.

2.2 DISPATCH soil moisture measurements

In this section we briefly describe the DISPATCH algorithm. Further details
can be found in Merlin et al. (2013) and references therein. The DISPATCH
algorithm aims to downscale NSSM data obtained from SMOS at 40 km resolution
to 1 km resolution. The method assumes that NSSM is a linear function of the
soil evaporative efficiency (SEE), which can be estimated at high resolution
(1 km) from the acquisition of two products obtained from MODIS, i.e., LST
and NDVI datasets. This MODIS-derived SEE is further considered as a proxy
for the NSSM variability within the SMOS pixel. The estimation of SEE is
assumed to be approximately constant during the day given clear sky
conditions. The downscaling relationship is given by Eq. (1):

(1)θHR=θSMOS+θHR′SEESMOS×SEEHR-SEESMOS,

where θSMOS is the low-resolution SMOS soil moisture
data, SEEHR is the MODIS-derived SEE at a high
resolution (1 km), SEESMOS is the average of
SEEHR within the SMOS pixel at a low resolution
(40 km), and θHR′SEESMOS is the partial derivative of soil moisture with respect to the soil
evaporative efficiency at high resolution evaluated at the
SEESMOS value. This partial derivative is typically
estimated by using the linear soil evaporative efficiency model of
Budyko (1956) and Manabe (1969), which is defined in Eq. (2):

(2)θHR=SEEHR×θp,

where θHR represents the soil moisture of the
top soil layer (0–5 cm) at high resolution, and θp is an empirical parameter that depends on soil properties
and atmospheric conditions. The soil evaporation efficiency at high-resolution SEEHR is estimated as a linear
function of the soil temperature at high resolution
(Ts,HR):

(3)SEEHR=Ts,max-Ts,HRTs,max-Ts,min.

The soil temperature at high resolution is estimated by partitioning the
MODIS surface temperature data (LST) into the soil and the vegetation
component according to the trapezoid method of Moran et al. (1994). This also
requires an estimation of the fractional vegetation cover, which is
calculated from the NDVI data. Ts,min and
Ts,max are the soil temperature end-members (Merlin et al.,
2012).

In this work, the DISPATCH algorithm has been applied during the period from
DOY36 to DOY298 of 2016 to estimate NSSM at 1 km resolution in the Foradada
field site. DISPATCH provides a daily NSSM pixel map (regular grid). The
Foradada field site is entirely included in one pixel. In this pixel,
51.5 % of the total area corresponds to irrigated area. The remaining
portion of the pixel corresponds to dry land (shown in Fig. 2).

Figure 3Comparison of average gravimetric soil moisture measurements (red)
with the DISPATCH soil moisture estimations (yellow) and the daily maximum
and minimum soil moisture sensor measurements (green) during the first
hydrologic period (soil wet conditions caused by rainfall events only).

2.3 Image spatial resolution and spatial variability

The information contained in a satellite image is characterized here by two
properties: the spatial resolution and the spatial variability of the image
attributes. The spatial resolution of a satellite image is the ground area
represented by each pixel, i.e., the raster cell size. It is essentially the
representative support volume chosen to describe the variations of the
attributes of interest at the ground surface. This is typically determined
based on the type of satellite sensor. The spatial variability refers to the
variations of the attributes presented in the image at the ground surface,
e.g., patterns of spatial continuity, size of objects in the scene, and so
on. In random field theory and geostatistics, the spatial variability is
mainly characterized by the covariance function or by its equivalent, the
semivariogram, which is defined by (Journel and Huijbregts, 1978):

(4)γh=12EZx+h-Zx2,

where Z(x) is the random variable at the x position, and
E{⋅} is the expectation operator. Essentially, the
semivariogram is a function that measures the variability between pairs of
variables separated by a distance h. Very often, the correlation
between two variables separated by a certain distance disappears when |h| becomes too large. At this instant, γ(h)
approaches a constant value. The distance beyond which γ(h)
can be considered to be a constant value is known as the range, which
represents the transition of the variable to the state of negligible
correlation. Thus, the range can ultimately be seen as the size of
independent objects in the image. If the pixel size is smaller than 10 times
the minimum range (in the absence of the nugget effect), then neighboring
pixels will be alike, containing essentially the same level of information
(Journel and Huijbregts, 1978). This will be a critical point in the
discussion of the results later on. We note that the spatial resolution and
the spatial variability are two related concepts. Several authors note that a
rational choice of the spatial resolution for remote sensing should be based
on the relationship between spatial resolution and spatial dependence
(Atkinson and Curran, 1997; Curran, 1988). However, since this is not the
usual procedure, the spatial resolution can be inappropriate in some cases or
provide unnecessary data in others (Atkinson and Curran, 1997; Woodcock and
Strahler, 1987).

3.1 General observations

One of the main advantages of our experiment is that remote sensing soil
moisture data is evaluated during two different hydrologic periods of the
same year in a given agriculture field site. The first period represents
crop growth with soil wet conditions caused by natural rainfall events
(without irrigation). This period occurs during the winter and spring
season, i.e., from February to June. The following period occurs during the
dry season with artificially created wet soil conditions caused by sprinkler
irrigation operating to satisfy crop water requirements during the summer
and autumn season, from June to October. In contrast to the rainfall events,
sprinkler irrigation creates a local artificial rainfall event using several
rotating sprinkler heads. The comparisons of these two hydrologic periods
allow us to evaluate the effect of local sprinkler irrigation on remote
sensing soil moisture estimations.

Figure 3 compares gravimetric and soil moisture sensor measurements with the
DISPATCH soil moisture estimates obtained from remote sensing data during the
first period of time (without irrigation). We note that the comparison here
is not between the point gravimetric measurements (with a support volume of
few centimeters) and the satellite information (1 km in resolution).
Instead, we compare the average of these point measurements over the entire
field site (very well distributed with more than 100 measurement points) with
the satellite information. The average of the soil moisture is representative
of the entire irrigated area associated with the Foradada field site.
Consequently, these two variables have similar support scale and are
therefore comparable. Error bars in the gravimetric measurements represent
the standard deviation of all the measurements obtained in 1 day. In
addition, the area between the light and dark green lines in this figure
displays the difference between the daily minimum and maximum values of soil
moisture data obtained from the five EC-5 sensors.
We note that the average of the gravimetric soil moisture data always lies
within this region. This supports the use of this information to complement
soil moisture data on days where no gravimetric sampling is available. The
error bars associated with DISPATCH data refer to the standard deviation
obtained with two daily SMOS estimations and four MODIS data (two at)
06:00 LST and two more at 18:00 LST). To better appreciate tendencies, the
same information is also presented as normalized relative soil moisture,
i.e., (θ-θmin)/(θmax-θmin), where θmin and θmax are the minimum and maximum
values of the soil moisture time series data obtained with the EC-5 sensors.
Results show that DISPATCH estimates can properly detect the relative
increase in soil moisture estimates caused by rainfall events. Note for
instance that all methods produce a similar relative increase in soil
moisture signal after the occurrence of a strong rainfall event. In absolute
terms, we see that DISPATCH slightly underestimates the true value of soil
moisture but this could be attributed to small differences between the
support volume of the field site and the spatial resolution of the satellite
image.

A similar analysis is shown in Fig. 4, which compares gravimetric and sensor
soil moisture measurements with DISPATCH soil moisture estimations during the
second period (wet soil conditions maintained by sprinkler irrigation). In
contrast to our previous results, it can be seen that the DISPATCH dataset is
essentially not sensitive to sprinkler irrigation even though there is a
proper response to sporadic small rainfall events. Likewise, the relative
increase in soil moisture measurements also shows that sprinkler irrigation
does not affect the DISPATCH estimation. Thus, even though the DISPATCH
estimations seem to properly respond to rainfall events during the first
period, irrigation operating at the Foradada field scale remains undetected
during the second period. The DISPATCH dataset is not sensitive to irrigation
and merely indicates that soil dry conditions exist at a larger scale.

Figure 4Comparison of average gravimetric soil moisture measurements (red)
with the DISPATCH soil moisture estimations (yellow) and the daily maximum
and minimum soil moisture sensor measurements (green) during the second
hydrologic period (soil wet conditions caused by irrigation). The top figure
shows the intensity of precipitation and irrigation.

This can also be seen from a different perspective by looking at the
scatter plot between the average of the normalized relative soil moisture
data obtained with the EC-5 sensors and the corresponding DISPATCH
measurement determined on the same day. Figure 5 shows the scatter plots
obtained during rainfall events and irrigation period. We note that even
though a clear tendency is seen during rainfall events (R2=0.57), no
correlation seems to exist during irrigation (R2=0.04). We conclude
then that the DISPATCH dataset provides representative estimates of soil
moisture at a lower resolution than expected.

Figure 5Scatter plot between the average of the normalized soil moisture
obtained with EC-5 sensors and the DISPATCH measurements obtained during both
hydrologic scenarios, rainfall events and irrigation period.

3.2 Analysis and discussion

We seek to answer the important question of why the DISPATCH soil moisture
estimates obtained by downscaling satellite information from 40 to 1 km of
resolution are not sensitive to sprinkler irrigation in this case. The
following possible sources of discrepancies can be identified: (i) errors
associated with the approximations used in the DISPATCH downscaling
formulation, (ii) differences in the scale of observations, (iii) low quality
of information associated with DISPATCH input variables, and (iv) poor
relationship between irrigation fluctuations and DISPATCH input variables
dynamics. We concentrate the analysis on (ii) and (iii). First, we note that
the DISPATCH resolution of 1 km is similar to the characteristic scale of
the irrigated area at the Foradada field site and therefore a better
performance was expected. The extent of the irrigated area in the DISPATCH
pixel size of interest is 51.5 % (see Fig. 2). Given that soil moisture
is a linear property, we contend that this cannot explain the negligible
relative increase in soil moisture obtained during irrigation. Then, we
examine the semivariograms of the different input variables involved in the
downscaling process, i.e., the NDVI and the LST properties provided by the
MODIS sensor. The NDVI and LST semivariograms were respectively estimated
from the MOD13A2 and MOD11A1 product data, which can be freely downloaded
from the Google Earth Engine website (https://earthengine.google.com,
last access: 15 January 2017). We selected daily representative images of
April, June and August. The April image describes a general rainfall event in
the region, the June image shows when local irrigation starts in the Foradada
field, and finally the August image represents when the crop is well
developed and frequent irrigation is needed. Experimental semivariograms have
been fitted with a theoretical model (spherical and exponential models for
the LST and NDVI, respectively), which can be formally expressed as Eqs. (5)
and (6):

where cij are constant coefficients that represent
the contribution of the different standard semivariogram models, and
aij denotes the corresponding ranges of the different
structures. The LST and NDVI experimental and theoretical semivariograms are
shown in Fig. 6. The parameters adopted in the random function model are
summarized in Tables 1 and 2. The analysis determines a nested structure
with a positive linear combination between isotropic stationary
semivariogram models and the hole effect model. Hole effect structures most
often indicate a form of periodicity (Pyrcz and
Deutsch, 2003). In our case, this periodicity reflects the presence of areas
with different watering and crop growth conditions, i.e., in contrast to the
dry-land conditions in the SG area, the Urgell area is based on irrigation.

The spatial variability of NDVI and LST vary with time according to changes
in hydrologic conditions. In April, the semivariogram of NDVI displays more
variability and less spatial continuity due to the differences in growth
rate and crop type conditions existing at the regional scale during the wet
season (controlled by rainfall events). On the other hand, the spatial
dependence of LST is more significant in August. Importantly, results show
that the scale of variability (range) associated with MODIS data during the
dry season, when a controlled amount of water by irrigation is applied,
ranges between 35 and 36 km for the NDVI and between 22 and 32 km for the
LST. Recalling the discussion provided in Sect. 2.3., this means that the
size of independent objects in the NDVI and LST images is about 30 km and
that insignificant spatial variations of NDVI and LST values are expected
below 1∕10 of this size. This suggests that the NDVI and LST products
provided by MODIS cannot detect differences between neighboring pixels with
a size of 1 km.

To further corroborate this point, Fig. 7 compares the temporal evolution
of LST and NDVI obtained from two adjoining MODIS pixels: the Foradada
pixel, where Foradada is located, and its northwest neighboring pixel. Note
that the neighboring pixel corresponds to an area that is not irrigated. Data
were downloaded using MOD13A2 and MOD11A1 products from the Google Earth Engine
website, from DOY036 to DOY298. In general, based on DISPATCH suppositions,
irrigation in an agriculture field site should produce a decrease in LST
values as a consequence of uniform irrigation over the entire field site and
an increase in NDVI due to well-developed crop growth conditions. However,
Fig. 7 shows the same dynamics and similar values in both pixels even when
irrigation is applied. Results show that the LST and NDVI information can
detect neither the sprinkler irrigation nor the crop growth as a consequence
of irrigation in this case. We finally note that these results suggest that
the resolution of LST and NDVI is not appropriate in this case but can also
express that these two variables are simply not sensitive to irrigation
because they only provide information about the status of the crop and land
surface. Further research is needed in this sense.

We analyze the value of remote sensing and the DISPATCH downscaling algorithm
for predicting soil moisture variations in an irrigated field site of size
close to image resolution. The DISPATCH algorithm based on the NDVI and LST
data obtained from the MODIS satellite is used for downscaling the SMOS
information and transforming the SMOS soil moisture estimations from a
resolution of 40 to 1 km. These estimates are then compared with average
gravimetric and soil moisture sensor measurements taken all over
the field site. Results have shown that in this case the downscaled soil
moisture estimations are capable of predicting the variations in soil
moisture caused by rainfall events but fail to reproduce the temporal
fluctuations in the average water content caused by local irrigation. To
provide insight into this problem, we examine the spatial variability of the
different input variables involved, i.e., the NDVI and LST. Results indicated
that the size of individual objects in the NDVI and LST images is too large
to be able to adequately represent the variations of the average water
content at the site. This effect is not significant during rainfall events
because the typical spatial scale of rainfall events is much larger than the
size of the irrigated field site.

From a different perspective, these results also suggest that irrigation
scheduling based on satellite information coupled with the DISPATCH
downscaling algorithm might be appropriate in regions of the world with
extensive irrigation surface coverage, larger than approximately 10 km
(e.g., Punjab basin). However, care should be taken when directly applying this method as its performance will strongly depend on the
spatiotemporal variation of irrigation within the area. These variations can
generate occasional areas with different hydrologic scenarios and behaviors
leading to the failure of the soil moisture prediction method.

We thank our colleagues from “Root zone soil moisture Estimates at the daily
and agricultural parcel scales for Crop irrigation management and water use
impact” (REC project), who provided insight and expertise that greatly assisted
the research.

Merlin, O.: An original interpretation of the wet edge of the surface
temperature-albedo space to estimate crop evapotranspiration (SEB-1S), and
its validation over an irrigated area in northwestern Mexico, Hydrol. Earth
Syst. Sci., 17, 3623–3637, https://doi.org/10.5194/hess-17-3623-2013, 2013.

One of the main objectives of remote sensing methodology is to downscale soil moisture to improve irrigation management. The DISPATCH algorithm is able to measure soil moisture at 1 km resolution using SMOS and MODIS data. In this work DISPATCH has been evaluated with soil moisture sensors, under heterogeneous conditions where local irrigation is applied. Results show that DISPATCH is not sensitive when local irrigation is applied even at low resolution.

One of the main objectives of remote sensing methodology is to downscale soil moisture to...